Remove Data Ingestion Remove LLM Remove Metadata
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The importance of data ingestion and integration for enterprise AI

IBM Journey to AI blog

In the generative AI or traditional AI development cycle, data ingestion serves as the entry point. Here, raw data that is tailored to a company’s requirements can be gathered, preprocessed, masked and transformed into a format suitable for LLMs or other models. Increased variance: Variance measures consistency.

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LlamaIndex: Augment your LLM Applications with Custom Data Easily

Unite.AI

In-context learning has emerged as an alternative, prioritizing the crafting of inputs and prompts to provide the LLM with the necessary context for generating accurate outputs. This approach mitigates the need for extensive model retraining, offering a more efficient and accessible means of integrating private data.

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Secure a generative AI assistant with OWASP Top 10 mitigation

Flipboard

Contrast that with Scope 4/5 applications, where not only do you build and secure the generative AI application yourself, but you are also responsible for fine-tuning and training the underlying large language model (LLM). LLM and LLM agent The LLM provides the core generative AI capability to the assistant.

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How Deltek uses Amazon Bedrock for question and answering on government solicitation documents

AWS Machine Learning Blog

Deltek is continuously working on enhancing this solution to better align it with their specific requirements, such as supporting file formats beyond PDF and implementing more cost-effective approaches for their data ingestion pipeline. The first step is data ingestion, as shown in the following diagram. What is RAG?

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How Twilio generated SQL using Looker Modeling Language data with Amazon Bedrock

AWS Machine Learning Blog

This post highlights how Twilio enabled natural language-driven data exploration of business intelligence (BI) data with RAG and Amazon Bedrock. Twilio’s use case Twilio wanted to provide an AI assistant to help their data analysts find data in their data lake.

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Drive hyper-personalized customer experiences with Amazon Personalize and generative AI

AWS Machine Learning Blog

You follow the same process of data ingestion, training, and creating a batch inference job as in the previous use case. They can also introduce context and memory into LLMs by connecting and chaining LLM prompts to solve for varying use cases. We are excited to launch LangChain integration.

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Personalize your generative AI applications with Amazon SageMaker Feature Store

AWS Machine Learning Blog

The personalization of LLM applications can be achieved by incorporating up-to-date user information, which typically involves integrating several components. These task-specific prompts are then fed into the LLM, which is tasked with predicting the likelihood of interaction between a particular user and item.